Application of hypothesis testing theory for optimal detection of LSB matching data hiding

This paper addresses the problem of detecting the presence of data hidden in digital media by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known digital medium parameters, two important results are presented. First, the use of hypothesis testing theory allows the design of the Most Powerful (MP) test. Second, a study of the MP test provides the opportunity to analytically calculate its statistical properties in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown medium parameters have to be estimated. Based on a local model of medium content, two different estimations which lead to two different tests are present. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

► Steganalysis is addressed using hypothesis testing theory.
► The statistical performance of the proposed test is analytically calculated.
► The proposed test permits the guaranteeing of a false-alarm probability.
► This provides an upper bound on the detection performance of any detector.
► Using general statistical concept it can be applied for a wide range of media.